Extraction of quantitative anatomical information from three-dimensional medical images and persistent storage and retrieval of this information has many applications in clinical diagnosis, drug development, and clinical research. As imaging devices, such as Magnetic Resonance (MR), computed tomography (CT), positron emission tomography (PET), and ultrasound, continue to improve in resolution and contrast, they are increasingly being used for these applications.
In clinical applications, the size and shape of an anatomical structure and its changes in size and shape over time inferred from medical images is often used an indicator of a disease condition. For instance, lesion size and its growth measured on CT images is indicative of cancer, a rapid enlargement of the prostate measured from ultrasound images is indicative of prostate cancer, and the shrinking of the hippocampus measured from MR images is associated with the onset of Alzheimer's Disease.
In all phases of drug development—drug discovery, animal studies, and pre-clinical and clinical trials—medical images are being increasingly used to study disease progression and to test the efficacy of a drug. Once again the goal is to measure the shapes and sizes and the changes in the shapes and sizes of anatomical and pathological structures for a large collection of patient image data. For example, the efficacy of a drug to treat multiple sclerosis is measured by the reduction in the sizes of the MS lesions on MR images of the brain.
The conventional practice is to extract size and shape information from images in most of these applications using either rough estimation based on appearance or painstaking manual outlining of anatomy or pathology on sequences of images. Both these currently practiced methods suffer from lack of accuracy and precision (due to inter-observer variability.) In addition, the latter method has a severe disadvantage in terms of the cost and time taken to extract size and shape information. Moreover, manual tracking of shapes and sizes in serial data is extremely difficult to do manually.
The conventional methods are deficient in that they do not allow the combination of various segmentation methods, registration methods, feature extraction methods, and database storage and retrieval. The conventional methods are further deficient in that features that have been extracted cannot be saved back into the database, linked, quantified and queried upon. Based upon these deficiencies alone none of these systems can perform a dual purpose as an image extracting and image mining system. Conventionally, two separate systems are employed that are not accessible or linked to each other concurrently.
In clinical practice there is also a need for the ability to register and quantitatively measure and compare lesions or tissues. A patient may undergo multiple scans at different time points to monitor disease or treatment progression. A system which can register these images and measure and compare disease progress can aid in the proper treatment planning of this patient.
The prior art is also deficient in that prior art systems do not allow quantitative tracking of shapes and sizes of anatomy or pathology over time. Furthermore, the prior art systems are deficient in that they do not provide a fast, automatic, multimodal registration method or process.
Several commercial medical imaging viewing stations are available which allow users to view images that are archived on a database. A review of these systems will emphasis their shortcomings. Some examples are PACS systems sold by GE, Marconi, Voxar, and Vital Images. All of these systems provide basic 2D and 3D image viewing capabilities and have databases attached to them that store the images and the patient data. Some of these products even provide some basic interactive segmentation and feature extraction methods. The GE system, for instance, even provides a registration method for multi-modality fusion.
However, all these systems are designed to handle individual patient data and therefore the reports generated are single patient oriented. All these systems use the database as nothing more than an image archive. The quantitative features extracted are not stored in the database so that they fail to link and quantify the data and do not support the mining of quantitative information. Moreover, none of these systems support tracking of serial/longitudinal data. Nor do the prior art systems provide multi-channel tissue classification or shape-based interpolation. There are several conventional systems that provide segmentation, registration, and visualization tools, for instance Analyze from the Mayo Clinic and 3D Viewnix from the University of Pennsylvania, just to name a few. These systems do not provide any level of database or image mining support.
It is desirable to provide a system that provides multi-channel tissue classification, region growing, interactive tracing for precise delineation of structure boundaries and shape-based interpolation utilizing semi-automatic tools for extraction and tracking of quantitative information from 2D or 3D medical images. It is also desirable to provide a system that provides the ability to view 2D or 3D medical images in various ways, fuse information from one or more imaging modalities, store and archive this information in a database, and discover patterns and trends in the data by mining this information and it is these ends that the present invention is directed.